Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors

This research introduces the Genetic Adaptive Scheduling System (GASS), a novel framework designed to optimize scheduling in Flexible Job Shop Scheduling Problems (FJSP). Due to its complexity, FJSP presents significant challenges stemming from machine flexibility, dynamic routing, and operation precedence constraints. GASS addresses these challenges by incorporating real-time, dynamic data, enabling the system to adapt to machine downtimes, fluctuating job priorities, and process variability. Leveraging advanced genetic algorithm techniques, GASS integrates enhanced mutation and selection processes that dynamically adjust setup times, prioritize urgent tasks, and balance machine workloads to minimize makespan effectively. Empirical results demonstrate that GASS achieves up to a 45.3% reduction in makespan within the flexible packaging industry, showcasing its ability to enhance scheduling efficiency and adaptability. The research highlights the system’s scalability and potential applicability across diverse industries, including printing, electronics, pharmaceuticals, and food manufacturing, where operational flexibility and efficiency are critical. By bridging existing gaps and integrating real-time constraints into scheduling models, GASS provides practical solutions for modern manufacturing environments. The findings contribute to the advancement of optimization techniques in FJSP, offering valuable insights for researchers and practitioners seeking efficient, scalable, and adaptive scheduling systems.